LLaDA2.0-mini

모델 소개

**LLaDA2.0-mini** is a diffusion language model featuring a 16BA1B Mixture-of-Experts (MoE) architecture. As an enhanced, instruction-tuned iteration of the LLaDA series, it is optimized for practical applications. + **Leading MoE Architecture**: The open-source **Mixture-of-Experts (MoE) diffusion large language model** continually trained on the Ling2.0 series with approximately **20 trillion tokens**. + **Efficient Inference**: With **16 billion total parameters**, only **1.4 billion** are activated during inference. LLaDA2.0-mini significantly reduces computational costs while outperforming open-source dense models of similar scale. + **Impressive Performance on Code & Complex Reasoning**: Excels in tasks such as **code generation** and **advanced mathematical reasoning**, demonstrating strong reasoning capabilities. + **Tool Use**: Supports **tool calling** and achieves excellent performance in complex agent-based tasks. + **Open & Extensible**: Fully open-source with commitment to transparency. We plan to release a **leading inference framework** in the future and continue investing in cutting-edge areas like **diffusion LLMs (dLLM)** to drive disruptive innovation. + **Supercharged Reasoning with LLaDA 2.0:** LLaDA 2.0 series will be fine-tuned with **Reinforcement Learning**, unlocking a new level of sophisticated reasoning and problem-solving abilities. + **Tools for Innovators:** The model was finetuned on the dFactory framework using Fully Sharded Data Parallel (FSDP2). We have begun open-sourcing dFactory and will continuously release our advanced post-training technologies. Whether you want to master the current model or build your own customized versions, you'll have the tools you need. Stay tuned for more updates!

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

자주 묻는 질문

출처 및 추적 기록

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。